Dmitry Nadezhkin

Learn More
The Process Network (PN) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is very difficult and highly error-prone task. To overcome the associated(More)
The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is very difficult and highly error-prone task. To overcome the associated(More)
Kahn Process Networks (KPN) are an appealing model of computation to specify streaming applications. When a KPN has to execute on a multi-processor platform, a mapping of the KPN model to the execution platform model should mitigate all possible overhead introduced by the mismatch between primitives realizing the communication semantics of the two models.(More)
Process Networks (PNs) is an appealing computation abstraction helping to specify an application in parallel form and realize it on parallel platforms. The key questions to be answered are how a PN can be derived and how its components can be realized efficiently on a given parallel system. In this paper we present a novel approach of communication model(More)
There is an increasing need for a framework that supports research on portable high-performance parallelism. Such a framework would facilitate two main goals: discovering how to separate application-specific code from hardwarespecific code, and supporting research on parallel schedulers and optimizations. The framework would be agnostic to language, however(More)
The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is a very difficult and highly error-prone task. To overcome the(More)
ion refinement techniques in probabilistic model checking are prominent approaches for verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a counterexample. This article proposes an abstraction refinement approach for the probabilistic computation tree(More)
  • 1